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, STOCKHOLM SWEDEN 2018
Decentralized Secondary Frequency Control in an Optimized Diesel PV
Hybrid System
ALICE VIEIRA TURNELL
KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ELECTRICAL ENGINEERING
AND COMPUTER SCIENCE
Decentralized Secondary Frequency Control in an Optimized Diesel PV
Hybrid System
Sekundärreglering i ett Optimerat Diesel PV Hybrid System
Alice Vieira Turnell
Examiner: Dr. Patrik Hilber Supervisors: M.Sc. Kateryna Morozovska
(KTH) M.Sc. Peter-Philipp Schierhorn (Energynautics GmbH)
A thesis presented for the degree of M.Sc. in Electric Power
Engineering
School of Electrical Engineering and Computer Science KTH Royal
Institute of Technology
Stockholm, Sweden August 2018
Abstract
This research argues that a diesel-based isolated electrical system
can be optimized by integrating a high share of solar photovoltaic
(PV) generation and that the frequency stability of such system can
be improved by including the PV participation in frequency
regulation. A case study is developed in order to explore an
island’s expansion of the installed generating capacity and its
optimization. This study uses the tool HOMER to solve the
optimization problem and PowerFactory to verify the frequency
stability of the proposed system. The PV integration allows for a
reduction of diesel fuel consumption, emissions and generation
costs. Additionally, in high PV penetration scenarios, the reduced
inertia in such systems can lead to high frequency deviations that
may trip the system protection. The study demonstrates that the
instantaneous frequency deviation after a load and generation
imbalance can be reduced by designing the PVs to operate with an
allocated reserve and a decentralized time-based secondary
frequency control. The frequency stability was achieved after
different disturbance scenarios under high PV penetration and
reduced available inertia, indicating that high PV integration is
economically and technically feasible in small island grids.
Keywords:Solar photovoltaic, frequency stability, decentralized
secondary frequency control, reserve allocation, hybrid system,
island system, HOMER, PowerFactory.
i
Sammanfattning
I detta examensarbete studeras hur ett dieselbaserat och isolerat
elsystem kan optimeras genom att integrera en hög andel solceller
(PV) i elproduktionen och att frekvensstabilitet kan förbättras när
PV användas i regleringen. En fallstudie har utvecklats under denna
forskning för att analysera en ökning av den installerade
generationskapacitet vid en ö samt hur detta kan optimeras. I denna
studie användas verktyget HOMER för modeloptimering och
PowerFactory för att testa den optimerade systemfrekvens
stabilitet. Med PV generation kan diesel konsumption, utsläpp och
kostnader minskas för hela systemet. En hög andel PV i generationen
reducerar elsystemet totala svängmassa vilket kan ledda till
avvikelser i systemfrekvensen som kan ursaka att skyddsystem
aktiveras. Studien demonstrerar att den momentana systemavvikelsen
efter en obalans kan reduceras genom att designa PV i systemet med
en allokerad reserv och en decentraliserad och tidsbaserad sekundär
frekvensreglering. Frekvensstabiliteten nåddes i olika obalans
scenarier med hög andel solcellgeneration och misnkat svängsmassa.
Detta tyder på att en hög andel PV integration är både ekonomisk-
och tekniskt möjligt i mindre elsystem.
Nyckelord: Solceller, frekvensstabilitet, decentraliserad sekundär
frekvenskontroll, reservallokering, hybridsystem, ösystem, HOMER,
PowerFactory.
ii
I dedicate this thesis to my parents, who have always encouraged
me. Especially to the memory of my father, who has seen the
beginning of this thesis journey and who has always
pushed me to aim higher and try harder, even if it took me far away
from home.
iii
Acknowledgements
I would like to express my sincere gratitude to my supervisors Msc.
Kateryna Morozovska and Msc. Peter-Philipp Schierhorn, as well as
to my examiner Dr. Patrik Hilber, for their support and knowledge
sharing during this research work.
My sincere thanks also goes to Dr. Thomas Ackermann and Dr.
Eckehard Tröster, who provided me with an opportunity to join their
team as a master student at Energynautics and develop my knowledge
skills in the area. I would also like to thank Daniel, Pablo and
all my colleagues at Energynautics, for their feedback and
cooperation.
Last but not least, I would like to thank my parents, David and
Maria de Fatima, my siblings Mariana, Mathew and Carolyn, my
stepfather Ian, as well as my other family members and friends, for
their motivation and support throughout this Master programme and
my life in general.
iv
Common Abbreviations
AGC Automatic Generator Control CRF Capital Recovery Factor DAPI
Distributed-Averaging Proportional-Integral DSL DIgSILENT
Simulation Language DSM Demand Side Management ENTSO-E European
Network of Transmission System Operators for Electricity ESS Energy
Storage System FCR Frequency Containment Reserves FRR Frequency
Restoration Reserves FSM Frequency Sensitive Mode HOMER Hybrid
Optimization for Multiple Energy Resources IRENA International
Renewable Energy Agency LCOE Levelized Cost of Energy MPP Maximum
Power Point MPPT Maximum Power Point Tracking NREL National
Renewable Energy Laboratory NPC Net Present Cost O&M Operation
and Maintenance PV Photovoltaic ROCOF Rate of Change of Frequency
RR Replacement Reserves STC Standard Test Conditions TSO
Transmission System Operator
v
List of Tables 2
List of Figures 4
1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . 1 1.2 Research Problem . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Research Goal
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2
2 Integration of a High Share of Renewables into Small Island Power
Systems 3 2.1 Design of the Hybrid Off-Grid System . . . . . . . .
. . . . . . . . . . . 4
3 Introduction to Solar PV Participation in Frequency Regulation 9
3.1 Frequency Stability . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . 9 3.2 Introduction to Solar PV Technology . . . . . .
. . . . . . . . . . . . . . 12 3.3 Solar PV Contribution to
Frequency Regulation . . . . . . . . . . . . . . 14
4 Research Methodology 21 4.1 Research Hypothesis . . . . . . . . .
. . . . . . . . . . . . . . . . . . . 21 4.2 Methodology Overview .
. . . . . . . . . . . . . . . . . . . . . . . . . . 21 4.3
Introduction to Case Study . . . . . . . . . . . . . . . . . . . .
. . . . . 23 4.4 Optimization of Generation Expansion . . . . . . .
. . . . . . . . . . . . 26 4.5 PV Frequency Control Strategy . . .
. . . . . . . . . . . . . . . . . . . . 33
5 Results and Discussion 40 5.1 Optimal Generation Installed
Capacity . . . . . . . . . . . . . . . . . . . 40 5.2 Frequency
Stability Analysis of Optimal Solution . . . . . . . . . . . . . 43
5.3 Feasibility Analysis of Results . . . . . . . . . . . . . . . .
. . . . . . . 57
6 Conclusions and Future Work 60
Bibliography 62
Appendix 67
List of Tables
2.1 Optimization problem’s structure for a hybrid power system
design. . . . 5 2.2 Off-grid systems in islands: successful
application examples. . . . . . . . 8
4.1 Characteristics of the pilot island. . . . . . . . . . . . . .
. . . . . . . . 23 4.2 Optimization problem structure. . . . . . .
. . . . . . . . . . . . . . . . 27 4.3 Load forecast scenario with
20% load increase per year. . . . . . . . . . . 28 4.4 Component’s
technical parameters. . . . . . . . . . . . . . . . . . . . . . 29
4.5 Rates for the system’s cost parameters. . . . . . . . . . . . .
. . . . . . . 29 4.6 Capital cost forecast of the hybrid system
components. . . . . . . . . . . 30 4.7 Operational costs of the
hybrid system components. . . . . . . . . . . . . 30 4.8
Optimization constraints. . . . . . . . . . . . . . . . . . . . . .
. . . . . 31 4.9 Comparison of features from different frequency
control strategies. . . . . 34 4.10 Definition of test scenarios. .
. . . . . . . . . . . . . . . . . . . . . . . . 39
5.1 Installed generation capacity in PowerFactory for the 2025
island’s system. 43
2
List of Figures
3.1 Example of frequency quality metrics. . . . . . . . . . . . . .
. . . . . . 11 3.2 Cumulative solar photovoltaic installed capacity
worldwide. . . . . . . . 13 3.3 PV operation outside maximum power
point. . . . . . . . . . . . . . . . 16 3.4 PV control topologies
for secondary frequency control, (a) centralized and
(b) decentralized. . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . 17 3.5 Primary and secondary control block diagram. . .
. . . . . . . . . . . . . 18 3.6 Time-dependent controller gain for
frequency control in single event and
multievent detection scenario. . . . . . . . . . . . . . . . . . .
. . . . . 20
4.1 Flowchart of the optimal scenario selection methodology. . . .
. . . . . . 22 4.2 Island’s demand measured sample during a partial
supply regime. . . . . 24 4.3 Island’s demand measured sample
during a 24 h supply regime. . . . . . . 24 4.4 Daily mean capacity
factor of a sample solar PV system in case study location. 25 4.5
Daily mean capacity factor of a sample wind turbine in case study
location. 25 4.6 Step-wise simulation inputs and outputs in HOMER.
. . . . . . . . . . . 32 4.7 Step-wise simulations’ cost and load
inputs. . . . . . . . . . . . . . . . . 32 4.8 Single line diagram
of island hybrid diesel-PV system of 2025. . . . . . . 38
5.1 Optimized installed capacities: system with and without
renewable sources. 41 5.2 Minimum renewable fraction achieved. . .
. . . . . . . . . . . . . . . . . 42 5.3 Optimized levelized cost
of energy: system with and without renewable
sources. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . 42 5.4 Optimized fuel consumption: system with and
wihtout renewable sources. 42 5.5 Percentage of solar production
curtailed. . . . . . . . . . . . . . . . . . . 43 5.6 Overview of
the PV participation in frequency regulation during a 6,5%
load variation and medium irradiance values (420 W/m2). . . . . . .
. . . 45 5.7 Overview of the PV participation in frequency
regulation during a 6,5%
load variation and high irradiance values (1000 W/m2). . . . . . .
. . . . 46 5.8 Total active power output of the PV units under
different frequency control
modes of operation, during a load variation of 13% and irradiance
600 W/m2. 47 5.9 Total active power output of the diesel generators
for the PV operation
under different frequency control modes, during a load variation of
13% and irradiance 600 W/m2. . . . . . . . . . . . . . . . . . . .
. . . . . . . 48
3
LIST OF FIGURES
5.10 Frequency response to a 13,5% load disconnection, for the PV
operation under different frequency control modes, during
irradiance 600 W/m2. . . 48
5.11 Frequency response to a 13,5% load reconnection, for the PV
operation under different frequency control modes, during
irradiance 600 W/m2. . . 49
5.12 Total active power output from the PVs, for different
allocated reserve levels, for a load variation of 30,1% and
irradiance of 600 W/m2. . . . . . 50
5.13 System’s under-frequency response for load reconnection event
of 30,1%, for different allocated reserve levels and irradiance of
600 W/m2. . . . . . 50
5.14 Active power output of one PV plant per affected group during
irradiance variations simulating a cloud movement. . . . . . . . .
. . . . . . . . . . 51
5.15 Active power and frequency response during an irradiance
variation in different PV units simulating a cloud movement. . . .
. . . . . . . . . . 52
5.16 Active power and frequency response to a 30,1% load variation
during high PV penetration (Irradiance 1000 W/m2). . . . . . . . .
. . . . . . . . . . 53
5.17 Active power and frequency response to a disconnection of one
PV plant during a high PV penetration scenario (Irradiance 1000
W/m2). . . . . . . 54
5.18 Active power and frequency response to a diesel unit
disconnection during a medium PV penetration scenario (Irradiance
600 W/m2). . . . . . . . . 55
5.19 Active power after the disconnection of one diesel unit,
during a medium PV penetration scenario (Irradiance 600 W/m2). . .
. . . . . . . . . . . . 56
5.20 Active power and frequency response to a large feeder
disconnection during a high PV penetration scenario (Irradiance
1000 W/m2). . . . . . . . . . 57
4
1 | Introduction
1.1 Motivation Amidst the current worldwide actions towards less
polluting energy systems, countries are setting ambitious targets
for the expansion of their renewable generation. The effects of
having a high share of renewable energy must be analyzed for each
individual system, in order to guarantee a successful generation
capacity expansion. Within this context, the Indonesian government
has set a renewable generation target of 23% of renewables in
produced energy by 2025.
Indonesia comprises several islands, many of which are isolated and
with electrical systems relying fully on diesel generators,
resulting in high generation costs. Designing the optimal
generation expansion for such isolated island systems, based on a
high share of renewable generation, will greatly reduce the
generation cost, contribute to achieving the aforementioned
national target and contribute to reducing greenhouse gas
emissions. Nonetheless, due to the lower availability of inertia in
systems with a high share of renewable generation, the design phase
must include a thorough analysis of the system stability and
reliability under different operating conditions.
The fast operation of inverter based generation technologies, such
as solar photovoltaic (PV)1 and wind power, despite not providing
inertia to the system, has the potential to improve the system’s
stability through the participation of such technologies in
frequency regulation, allowing for frequency stabilization and
restoration. Whereas the secondary fre- quency reserves in larger
systems are usually automatically dispatched, in smaller systems a
manual dispatch might occur. Methods are being researched for a PV
decentralized sec- ondary frequency control, with no communications
required for the dispatch. Decentralized methods represent an
alternative strategy for systems with unreliable communication
chan- nels, in order to avoid further instabilities caused by
communication delays or interruptions in the channels which are
used for the centralized generation dispatch.
1The term PV, used hereafter in this report, comprises the
conversion of solar energy into DC power and the conversion of the
resulting DC power into AC power.
1
Chapter 1. Introduction 2
1.2 Research Problem Feasible methods must be sought to
successfully integrate high shares of renewable genera- tion in
isolated electrical systems.
The technical and economic impacts of having high shares of
renewable generation in isolated small, or very small, grids need
to be further investigated, taking into account each system’s
unique characteristics and targets. For isolated communities, the
design of the capacity expansion must also account for the cost of
transporting equipment and fuel to remote locations, which directly
impacts the final generation cost. Additionally, the reduced
inertia in small island grids with a high share of renewables
requires further system stability analysis to ensure that the
designed system is capable of maintaining stability under different
disturbance scenarios. Finally, isolated systems with unreliable
communication channels require additional measures for frequency
regulation. For optimal system design, a balance must be sought
between the stability, reliability and costs of the proposed
system.
1.3 Research Goal The primary objective of this Master’s thesis is
to propose a technically and economically feasible generation
capacity expansion plan for a very small off-grid system, which
will yield the targeted share of renewable generation whilst
ensuring frequency stability.
In the first step of this case study, specific economic and
technical data is used to structure, and solve, an optimization
problem yielding an optimal capacity expansion plan for a small
tropical island. In the second step the optimization problem
solution is further analyzed using dynamic simulations in order to
verify the system’s frequency stability under different scenarios
in which the PVs are contributing to frequency regulation.
The specific research goals are:
• to structure and solve a capacity expansion optimization problem
for an isolated hybrid power system that will meet a proposed
renewable target
• to identify the frequency stability challenges encountered with
the expansion of a small island hybrid system with a high share of
renewables
• to propose a decentralized frequency regulation strategy for
off-grid systems with unreliable communication channels and verify
if the frequency stability can be improved when PVs participate in
frequency regulation
2 | Integration of a High Share of Renewables into Small Island
Power Systems
Small off-grid electrical power systems, such as those in small
islands, are still predom- inantly dependent on fossil-fuelled
generators, more specifically oil-based. Utilizing oil-based
generation impacts on current emission reduction targets and
results in high generation costs. Fuel transportation to remote
locations further increases generation costs. The reduction of
technology costs of renewable generation combined with the emission
reduction targets make hybrid off-grid systems with a high share of
renewable generation complemented by fossil-fuelled generation an
attractive solution for isolated electrical systems.
Integrating a high share of intermittent renewable energy sources,
such as solar and wind energy, into small scale off-grid systems
poses a time compatibility challenge to meeting the demand
distribution. In addition to the intermittent characteristic of
these sources, the lack of inertia provision due to the power
electronic interface represents a stability challenge for
large-scale renewable integration. Nonetheless, when adequately
combined with other generation sources, a high share of renewable
generation can yield an efficient and reliable power supply
[1].
Hybrid off-grid systems must be designed to optimize the available
resources, according to the economic and technical characteristics
of each specific system and location. In this case study, the
optimization of the generation expansion will be performed for the
off-grid system of a remote tropical small island in Indonesia. The
island, with 13000 inhabitants and 0,8 MW peak consumption, is
currently supplied by two diesel generators during 12 hours per
day, and has a (high) cost of 0,21 EUR/kWh. The expansion plan is
part of a pilot study in Indonesian islands, to contribute to the
country’s renewable target. Further characteristics of the case
study system will be addressed in Section 4.3.
This chapter addresses considerations for the design of an off-grid
hybrid power system comprising PVs and fossil fuel-based
generation. It includes an introduction to the challenge of
maintaining system stability in small hybrid power systems which is
further explored in Chapter 3 along with the latest research on PV
participation in frequency regulation. Solar PV is the renewable
technology at focus in this research, as the potential for wind
energy is low in the case study.
3
Chapter 2. Integration of a High Share of Renewables into Small
Island Power Systems 4
2.1 Design of the Hybrid Off-Grid System Efficient design and
planning of a hybrid power system requires optimization of the
system’s Net Present Cost (NPC) whilst ensuring reliability of the
system [2]. The NPC is defined as the ratio of total annual costs
of the system to annual electricity delivered. It equals the total
discounted present values of all the future costs during the
system’s lifetime [3]. The NPC includes the initial costs of
components, replacement costs and maintenance costs.
An optimization problem must be structured and solved in order to
obtain the optimal solution for the hybrid system design. It must
consider the installed capacity per generation source which will
yield the lowest system NPC whilst meeting (i) the load, (ii) the
renewable energy production target and (iii) the spinning reserve
constraints. The level of details in the optimization problem
structure and the accuracy of the input data will determine the
quality of the obtained solution. Moreover, decisions regarding
energy storage systems, demand side management and reliability
requirements, must be made when structuring the optimization
problem. A brief overview on these topics is given in the following
subsections, as well as a summary of successful application cases
in which a high share of renewable energy has been integrated into
island off-grid systems.
2.1.1 Optimization Problem for the Generation Expansion A variety
of computational tools exist for structuring and solving
optimization problems. The overall problem structure comprises an
objective function containing the variable to be optimized either
through minimization or maximization, as well as the definition of
parameters, variables, constraints and limits. The optimization
problem for a hybrid system’s generation expansion can be
structured as illustrated in Table 2.1.
2.1.2 System Stability and Reserve Requirements Hybrid systems must
be carefully designed to ensure system stability. The stability of
a power system corresponds to its ability to regain a state of
equilibrium after the occurrence of a physical disturbance. When a
disturbance occurs, the kinetic energy stored in the rotating
masses of machines is the immediate system inertial response and
contributes to reducing the frequency instability. Frequency
controllers then act through the adjustment of the generators’
production in order to stabilize the frequency variation and return
it to the system’s nominal value. An increase in the system’s load
will reduce the system’s frequency and, in order to adjust their
production upwards, the generators participating in frequency
regulation must operate with a reserve capacity.
Small isolated power systems have an additional challenge regarding
system stability because the loss of a generating unit represents a
greater share of the total generation. The reduced inertia in such
systems results in a higher rate of change of frequency (RoCoF) on
the occurrence of a disturbance than would occur in interconnected
systems [4].
Fossil-fuelled generators can provide the response required for
primary frequency control when their ramping capability is
well-dimensioned for the application. A fast ramping capability
allows for compensation of fast load or generation variations such
as those caused by cloud movements in PV energy production.
Therefore, higher ramping
Chapter 2. Integration of a High Share of Renewables into Small
Island Power Systems 5
Table 2.1: Optimization problem’s structure for a hybrid power
system design.
Category Items
Objective function
Meteorological data (wind speed, solar irradiance and temperature)
Load Profile (hourly demand profile, seasonal and daily
variability) Costs (initial capital costs of equipment, operation
and maintenance, replacement and fuel costs) Equipment parameters
(efficiency, lifetime, minimum power output limit, minimum
operating hours) Project lifetime Load consumption projection (kWh)
Economic parameters (expected inflation rate and nominal discount
rate)
Variables to Installed capacity per generation type be optimized
Production per generation type
Levelized Cost of Energy System’s Net Present Cost Renewable energy
fraction Total fuel consumption Curtailed PV generation
Constraints Minimum share of renewable generation target Maximum
annual capacity shortage
Limits Available resources on-site
Chapter 2. Integration of a High Share of Renewables into Small
Island Power Systems 6
capability of fossil-fuelled generators allows for higher renewable
energy integration of PVs and wind power.
In contrast to PVs, wind power plants can provide synthetic
inertial response when operating at their maximum power point, due
to the kinetic energy stored in the turbine blades. However, this
initial response can only last a few seconds as the kinetic energy
must be restored quickly. Nevertheless, both PV and wind power
plants can provide frequency regulation by allocating part of the
installed capacity as a reserve.
Different frequency control strategies have been proposed in order
to allow renewable generation to participate in frequency
regulation. According to Liu et al. (2017) in [5], due to its
simplicity and robustness, strategies with droop-based control are
more likely to be first applied in the market. The latest
strategies for PV participation in frequency regulation will be
introduced in Chapter 3.
Energy Storage Considerations
In hybrid systems containing PVs, the utilization of Energy Storage
Systems (ESS) can aid in improving system performance. The excess
of PV generation can be stored in the ESS, thus reducing or
avoiding a curtailment. The ESS also contributes to balancing short
term fluctuations from the load/generation balance.
Storage systems can be included in the generation optimization
problem and the optimal solution will indicate the need for such
storage based on the system’s technical and economic
characteristics. ESS can be of chemical nature, electrical or
thermal, among others [6]. The optimal ESS will be determined based
on the application. Currently, batteries are the most commonly used
storage technology in off-grid hybrid systems. This is due to the
maturity of the technology and market costs. The battery system
must be specified in order to provide adequate system stability
with minimum resulting LCOE [7].
In PV generating units, power fluctuations occur in the inverter
output due to PV intermittency. A storage unit can be connected
directly to the DC link of the PV inverter in order to limit the
power fluctuations. Nonetheless, a centralized energy storage
system is preferred for PV plants with more than one inverter,
since PV power fluctuations at the Point of Common Coupling (PCC)
with the grid will reduce with increasing PV plant site. Therefore,
when choosing to limit the fluctuations at the PCC with the system,
the energy storage requirement is lower than when connecting it at
the inverter level [8].
2.1.3 Demand Side Management Potential in Remote Areas In order to
increase the system’ stability in off-grid small scale energy
systems based on a high share of renewable energy, Demand Side
Management (DSM) can be used to obtain flexibility on the demand
side to meet the fluctuations of the intermittent generation share.
Demand response technologies offer the possibility of improving the
system’s reliability, reducing peak demand and reducing
corresponding capital investments.
Different techniques for DSM exist, in which the demand can be
controlled directly through controllers or by a customer response
to variations in price. For systems located in tropical weather
areas, there is a great potential for DSM through the control of
space cooling and refrigerating machines, as well as water supply
pumping hours.
Chapter 2. Integration of a High Share of Renewables into Small
Island Power Systems 7
2.1.4 System Reliability Requirements A hybrid system must be
designed to supply the loads at the lowest system’s cost whilst
still ensuring a reliable system. The intermittent characteristics
of solar and wind resources represent a risk for the system’s
reliability and must be compensated for in the system’s
design.
Different methods exist in order to analyze the reliability of
hybrid systems. These include: loss of power supply probability
(LPSP), loss of load probability (LOLP), unmet load (UL) and loss
of load risk (LOLR). LPSP is the ratio of the power supply deficits
to the electric load demand during a time period. LOLP is the ratio
between power failure time period and the total working time of the
system. Unmet load is the load that is not served regarding the
total load of a certain time period. LOLR is the probability of the
generating system not meeting the daily demand due to deficit of
energy from the renewable power supplies used [9].
In [10], LPSP has been used to assess the reliability of a power
supply for a hybrid off-grid system and determine the size of the
system’s components that would yield the highest economically
attractive and reliable system.
2.1.5 Successful Application Cases: Island Hybrid Systems with High
Renewable Share
Pilot projects for hybrid systems with a high share of renewables
have been conducted in different islands. Their results and
learning outcomes are a useful source for new projects. Four
successful application projects which have published results will
be briefly discussed in this section. These are the systems of the
islands: King Island, St Eustatius, South Tarawa and Madeira.
Further characteristics of these island systems are summarized in
Table 2.2.
King Island’s generation was fossil-fuelled until the year of 1998.
The island’s hybrid wind-biodiesel system with energy storage and
demand side management allowed for a 45% fossil fuel reduction. Its
wind power supplies up to 70% of the demand and an occasional
operation occurs with zero diesel generation due to the
Uninterruptible Power Supply (UPS) and flywheel system. Adequate
battery sizing and type selection was one of the challenges
reported in the project. The initial batteries selected – Vanadium
Redox Batteries – presented cell stack failures as well as several
inverter failures. These were later replaced by lead-acid
batteries. An additional challenge identified was the high cost of
electricity supply when compared to the income from the electricity
sale. The difference is currently reimbursed by the government and
charged as a community service cost [11].
St Eustatius Island’s generation was fossil-fuelled until 2015. A
solar-diesel hybrid system with energy storage was commissioned
there in 2016, reducing the fossil fuel consumption by 23%. Periods
with up to 88% of the load being supplied by the solar PV
production were observed [12].
South Tarawa’s hybrid solar-diesel system was commissioned in 2015
and 227000 liters of fossil fuel consumption reduction was
achieved, coupled with a contribution to the electricity production
of 8% from solar panels [13]. With no ESS, a high level of
curtailment occurs. An analysis performed by the International
Renewable Energy Agency (IRENA) estimated that a level of
contribution to the island generation of approximately 35% would be
achieved by adding 2,5 MW of Photovoltaic (PV) capacity and 2,64
MW/
Chapter 2. Integration of a High Share of Renewables into Small
Island Power Systems 8
5,6 MWh of ESS [14]. Madeira island has a target of achieving 50%
contribution from renewable energy
generation to the energy mix by 2020. In 2017, a 30% contribution
was achieved with 17,96 MW of installed PV systems [15],
[16].
These examples serve as lessons learned for high renewable
integration projects in islands. A balance between the technical
and economic characteristics of the system design must be sought,
in order to avoid high generation costs such as the ones observed
in King Island. An optimization problem can be solved to determine
an optimal capacity expansion for each system. The PV production
curtailment can be reduced by adding an ESS, as indicated in South
Tarawa Island. Madeira island indicates the possibility of
achieving a high renewable target with a hybrid system diversified
with different generation sources. St Eustatius island is similar
to the case study in this research and indicates that high PV
penetration is possible in a hybrid diesel-PV-ESS system. The ESS
in St Eustatius compensates for small load/generation imbalances
with no frequency regulation contribution from the PV system.
Table 2.2: Off-grid systems in islands: successful application
examples.
King Island St Eustatius South Tarawa Madeira Island
Population 1 800 (2013) 3 877 (2015) 50 180 (2010) 262 456 (2011)
Country Australia Caribbean Kiribati Portugal Load 3,3 MW peak 2,3
MW peak 17,3GWh (2011) 820,8 GWh Wind Turbines 2,45 MW - - 45,11 MW
PV System 100 kW 1,9 MWp 1,5 MWp (2016) 17,96 MW Diesel Genera-
tors
6 MW 4 MVA 5 MW Thermal: 218,7 MW
Other generation sources
ESS (batteries) 3MW/ 1,6MWh 1MW/ 580kWh; - - Dynamic resis-
tor
1,5 MW - - -
3 | Introduction to Solar PV Participation in Frequency
Regulation
Whilst rotating machines accomplish the secondary frequency control
within a few minutes, in PVs, the converter based technology has
the potential to perform this operation within a few seconds [17].
A literature review of the contribution of PVs to frequency
regulation will be presented in this chapter, focusing on the
recent research area of PVs’ contribution to decentralized
secondary frequency control. The theoretical basis, and
terminology, of frequency regulation will also be introduced.
3.1 Frequency Stability
3.1.1 Frequency Regulation Dynamics Using the terminology from the
European Network of Transmission System Operators for Electricity
(ENTSO-E), frequency control can be performed in three stages, each
of which with a designated reserve allocation, namely: Frequency
Containment Reserves (FCR), Frequency Restoration Reserves (FRR)
and Replacement Reserves (RR). The terms primary, secondary and
tertiary control are also used.
In the first few seconds after a disturbance in the active power
balance occurs, the dynamic behaviour of the system is given by the
Inertial Frequency Response (IFR). The IFR comes from the kinetic
energy stored in the rotating masses of the synchronous generators
connected to the system. This response opposes the fast frequency
deviation and thus reduces the Instantaneous Frequency Deviation
(IFD) reached. The IFD should not exceed the maximum permissible
limit as it may trigger protection systems, causing further system
instabilities which can lead to black outs. Maintaining an adequate
amount of inertia in the system is important as it takes a few
seconds before the activation of FCR and thus, within this time,
IFR is the only opposition to the frequency deviation. Therefore,
the inertia available in a system directly impacts the rate of
change of frequency in the system as well as the IFD [18].
FCR is activated in order to stabilize the system frequency within
the maximum allowed Steady-State Frequency Deviation (SSFD) value,
whereas FRR is utilized to regulate
9
Chapter 3. Introduction to Solar PV Participation in Frequency
Regulation 10
the frequency error to zero and replace the activated FCR. RR
control has the target of restoring FRR progressively and
supporting FRR activation [19]. RR control coordinates the
operation of the power flows in a grid, in order to achieve the
optimal economic dispatch among the generating units as well as
meeting grid constraints such as reactive power flows and
corresponding voltages. The control layers have different response
speeds and thus have separate dynamics [20].
The activation process for FCR is performed within seconds and
corresponds to an automatic response, commonly from the mechanical
speed governor control in the rotating generators. It utilizes a
pre-defined frequency droop in order to achieve active power
sharing among the generators participating in the control stage.
The activation process for FRR takes between a few seconds and a
few minutes and can either be manually activated or performed by
the Automatic Generation Control (AGC), by activating a
supplementary control loop which integrates the frequency error.
The FCR full activation time requirement varies for different
synchronous area sizes. For instance, for continental Europe, [19]
requires the activation time to be within 30 seconds, whilst for
Great Britain it is within 10 seconds.
3.1.2 Frequency Regulation Requirements for Generators Network
Codes specify guidelines for the connection of generators, which
will be a function of the generator type. Different units may have
different droop and activation times in order to reach the overall
droop required by the system. Published guidelines for the Network
Code, given by the ENTSO-E in [21], discuss the requirements for
frequency regulation, some of which will be introduced in this
section.
Limited Frequency Sensitive Mode - Over-frequency (LFSM-O)
In the LFSM-O, the PVs would be required to automatically reduce
the active power output in case of over-frequency, due to a large
loss of demand or a sudden production increase, in order to reduce
the IFD. The size of the synchronous area impacts the frequency
threshold settings, with higher values for smaller areas in the
LFSM-O. The frequency threshold for the LFSM-O, mentioned in [21],
corresponds to a value within the interval [50,2, 50,5] Hz, thus
[0,004 , 0,01] p.u., and a droop within [2, 12]%.
Limited Frequency Sensitive Mode - Under-frequency (LFSM-U)
In the LFSM-U, generating units must increase their active power
output in case of under- frequency due to a severe loss of
generation or load increase. The generation increase is
proportional to the frequency variation and activated as fast as
technically possible. Different power generating units can have
different droop settings and activation times, in order to reach
the system’s required droop without unnecessary protection tripping
and loss of load. Failure to stabilize the frequency will lead to
frequency collapse, with consequences such as cascade tripping,
load shedding and black outs. The operation of PVs in the LFSM-U is
possible if they operate below their maximum power point, i.e. with
a reserve capacity. The frequency threshold for the LFSM-U,
mentioned in [21], corresponds
Chapter 3. Introduction to Solar PV Participation in Frequency
Regulation 11
to a value within the interval [49,8, 49,5] Hz, thus [0,004 , 0,01]
p.u., and a droop within [2, 12]%.
Rate of Change of Frequency (RoCoF) Withstand Capability
The RoCoF is the time derivative of the system frequency (df/dt),
being used as a metric of system inertia by network operators to
guide the control actions to maintain the stability of the system.
Networks with a high penetration of inverter-based generation have
a larger RoCoF because the inertia is low.
The RoCoF withstand requirement is to ensure that PVs will remain
connected to the system in case of abrupt frequency variations up
to a set-point df/dt (determined by the TSO). This must be done by
neglecting short term electro-mechanical effects, and thus allowing
the primary frequency control to react, avoiding a cascading effect
of generation loss. This requirement must be carefully coordinated
with the protection settings [22].
The RoCoF withstand capability is determined by the TSO, based on
the characteristics of the entire synchronous area. In small
systems with low inertia, the capability can be determined based on
the loss of the largest power generator unit when the inertia in
the system is low [22].
RoCoF thresholds of ±2Hz/s for a moving average of 500ms window are
suggested by ENTSO-E in [22]. Power generation modules are allowed
to disconnect if the frequency varies beyond this threshold. These
limits are listed as a general reference and are not applicable to
small systems with low inertia.
3.1.3 Frequency Control Quality Metrics Three parameters are
commonly used to evaluate the frequency response in a system: RoCoF
(δf/δt), IFD (called frequency nadir for under-frequency events or
zenith for over-frequency events) and Steady State Frequency
Deviation (SSFD) [23]. These are exemplified in Figure 3.1 for an
under-frequency scenario.
c© 2017, IEEE
Figure 3.1: Example of frequency quality metrics. Source:
[23].
Chapter 3. Introduction to Solar PV Participation in Frequency
Regulation 12
3.1.4 Requirements for an Adequate Frequency Measurement The
electrical frequency in a system can be measured for different
purposes, such as for power frequency control and power system
protection. The frequency measurement is a fundamental stage of the
frequency control and thus must adequately reflect the system
frequency for this purpose.
Different phenomena have different time constants and require a
specific measurement setting that results from a compromise between
speed and accuracy. In [24], ENTSO-E has published reference
parameters that cover compliance tests and monitoring needs
according to the intended use of the measurement. The recommended
measurement window for decentralized generation control is 100 to
200 ms, with an accuracy of 10 mHz.
Some phenomena, such as short circuits and transformer
energization, can lead to errors in the local frequency
measurement, due to phase jumps resulting from discontinuities near
the frequency measurement point. In applications that require a
fast response, less filtering is required causing these local
events not to be distinguished from the global events. In a
decentralized frequency regulation process, the localized frequency
error will be contained in the PV plant near the measuring point.
Erroneous measurement may however lead to the disconnection of PVs
plants [24].
In a system with synchronous generators, the frequency variations
are limited by the inertial response from the rotating masses,
which allows the frequency to be calculated over a longer period
thus obtaining smoother measuring results [24]. In an island system
with a high share of PVs and thus reduced inertia, the frequency
measurement sampling rate must be sufficiently high to allow fast
reaction of these generators without causing unnecessary reactions
to small frequency variations. Although the frequency measurement
technique is not the focus of this research, the effects of
frequency measurement errors can be considered in future
analysis.
3.2 Introduction to Solar PV Technology With the rapid expansion of
solar photovoltaic installed capacity worldwide, reaching 384,6 GW1
in 2017 [25], as depicted in Figure 3.2, the impact of their
penetration is significant on power systems. Several grid codes
include guidelines for PV connection in order to maintain system
stability and reliability. Fault ride through capability, reactive
power compensation and frequency regulation performed by solar PV
systems must be addressed.
A PV module can be described by a set of equations, which will be
briefly introduced in this section and later used in the design of
the PV model in the power system analysis tool.
Photovoltaic cells contain p-n junctions which generate an electric
current when exposed to light. The photo-electric current of a PV
module, Iph, equals the short circuit current of one string2
corrected by a temperature correction factor in order to
incorporate scenarios which differ from the standard test
conditions (STC). Iph is given by Equation 3.1 [26]:
Iph = [Isc +KI(T − Tref )]E, (3.1)
1Does not include concentrated solar power installed capacity. 2A
string of PV corresponds to the PVs which are connected in
series.
Chapter 3. Introduction to Solar PV Participation in Frequency
Regulation 13
c© IRENA
Figure 3.2: Cumulative solar photovoltaic installed capacity
worldwide. Image generated from the IRENA database. Source:
[25].
in which T is the temperature [kelvin], Isc is cell short circuit
current [A], Tref is the reference temperature [kelvin], KI is a
temperature coefficient and E is irradiance [W/m2].
The PV array current can be sized by adjusting the number of cells
in parallel and the voltage can be sized by adjusting the number of
cells in series. The output current of the PV array, Ipv is given
by Equation 3.2 [26]:
Ipv = Np
( Iph − Irs
) − 1 )) , (3.2)
in which Np is the number of cells in parallel, Ns is the number of
cells in series, Irs is the reverse saturation current [A], q is
the electron charge [1,602 × 10-19 C], k is Boltzmann’s Constant
[1,381 × 10-23 J/K], A is the ideality factor and Vdc is the open
circuit DC voltage [V].
The output DC voltage at maximum power point, VMPP , can be
modelled by Equation 3.3 [26]:
VMPP = VMPPO
) (1 + αv(T − TSTC)), (3.3)
in which αv is the temperature correction factor and VMPPO is the
output DC voltage [V] at maximum power point for standard test
conditions (STC), i.e. temperature TSTC of 25C and irradiance of
1000 W/m2.
Among the data needed for modelling the PV, only a few are
available on manufac- turer’s technical data sheets. The remaining
values must be calculated analytically or via
Chapter 3. Introduction to Solar PV Participation in Frequency
Regulation 14
optimization techniques [27]. The voltage and current of the PV
depend on the temperature and irradiance at each
moment. Under uniform test conditions of ambient temperature and
solar irradiance, there is a unique operation point that yields
maximum power and thus maximum efficiency, regardless of the load
[26]. Because this Maximum Power Point (MPP) is continuously
varying with temperature and irradiance, PV’s are equipped with a
Maximum Power Point Tracking (MPPT) control technique to track the
MPP and thus output the maximum power available at each moment. The
MPPT control will find the optimal value of the DC output voltage
(or current), based on measurements of the module current and
voltage which reflect the updated weather conditions, and send it
as a reference to the main controller so that it regulates the DC
voltage (or current), in order to achieve the MPP.
Different MPPT techniques exist. In [27] the most common and recent
techniques are classified into conventional and soft computing
techniques. The authors provide a comparative review of these
techniques, regarding tracking speed, algorithm complexity,
tracking under partial shading and hardware implementation.
Regarding the connection to the electrical system, PV panels are
connected to a DC bus, to which they transfer the generated DC
power converted from the solar energy. A DC-AC inverter connected
to the DC bus and to an AC bus, will then convert the DC power into
AC power, in order to connect the PV system to the AC electrical
system. The MPPT control is performed by the inverter, through the
control of the DC voltage. A capacitor is used to smoothly adjust
power fluctuations between the AC and DC sides.
According to how the PV array is configured, different topologies
for inverter connection can be used. Among these, a central
inverter is commonly used which has high efficiency and low cost.
This is because a single converter is used, with several PV modules
connected in series (representing a string) and several strings
connected in parallel.
3.3 Solar PV Contribution to Frequency Regulation The most recent
grid codes are starting to require, or to recommend, frequency
regulation participation from PVs, through operation reference
points set by the transmission system operator [28]. The control
settings of the frequency response are crucial in order to achieve
a fast frequency response without causing further instability in
the system, especially in low inertia systems.
In droop-based control methods, the inverter measures the grid’s
frequency on the AC side of its terminals and modulates the output
power according to the droop settings, which can be pre-programmed
with the TSO droop curve reference. This will re-establish the
balance between generation and load. However, this will not restore
the frequency to the nominal value.
The NREL has performed power hardware-in-the-loop (PHIL) tests on
different PV inverters currently available in the market, connected
to the grid system of the island of Oahu in Hawaii. The tests
performed confirmed that these inverters are currently capable of
providing frequency support for over-frequency events, using a
droop setting method. However, different inverters perform it
differently, since this is often not a requirement in grid codes
and therefore there is not one standardized method. Regarding
under-frequency
Chapter 3. Introduction to Solar PV Participation in Frequency
Regulation 15
control, it was identified as technically possible but, requires
part of the PV capacity to be allocated as reserve, with the PVs
operating below their maximum power point [29].
Furthermore, as described in [29], it was found through simulations
and tests that:
• Most inverters are not configured to provide frequency support,
therefore already installed PVs would have to be reconfigured
whereas it is recommended that future installed PVs have their
droop control activated upon commissioning.
• When PVs are configured with stepper droop curves, the nadir
frequency reduces. However, simulation results indicated the
occurrence of higher oscillations, especially when the amount of
PVs contributing to frequency regulation is high.
• When using droop-based control with PVs a dead-band must be used
in order to avoid excessive actions from the governor control due
to small variations in frequency during normal operation. This
dead-band must be similar or smaller than the dead- band of
synchronous generators’ governor [5]. Simulations in [29] indicated
that narrower dead-bands yield a better frequency response.
However, the dead-band must be sufficiently sized so that the
function is not activated by typical frequency fluctuations.
• PV inverters have demonstrated in the tests that they are capable
of fast power ramp- ing, thus the settings on the time response
must be fast regardless of the magnitude of the power change,
especially in islands with low inertia. The droop recommendation of
the study was between 5% and 3%. These values are aligned with the
droop set- tings of synchronous generators therefore yielding a
sharing of the primary frequency response. The time response of the
droop control for the inverter to perform 90% of the power change
is commonly recommended to be below 2 seconds. Lower values are
indicated to yield better results. However the interaction with the
synchronous generator controls should be carefully analyzed.
Operation Below the MPP (Reserve Capacity)
When PV systems are set to operate at the point of maximum power
extraction from the modules, this results in no additional capacity
available for frequency control in under- frequency events. In
[17], a strategy is proposed to allocate part of the PV-generated
power as reserve capacity, which can be injected into the system
for frequency control, improving the system’s stability and
reducing the need for a storage system or reducing the
charging/discharging cycles of existing storage. The power of the
PV module is a function of three main parameters: temperature,
irradiance and output voltage (or current), as seen from Equations
3.2 and 3.3 in Section 3.2. The strategy proposes to change the
output power by adjusting the duty cycle of the converter so that
the output voltage is a fraction of the total possible voltage. The
remaining fraction is allocated as reserve. The research results
showed less stress on the conventional power plants due to reduced
power variations as well obtaining faster frequency response with
the contribution of PVs to both primary and secondary frequency
control.
Figure 3.3 exemplifies the shift in the operating point of the PV
from the point of maximum power (with voltage and power: VMPP and
PMPP ) to the de-loaded operation
Chapter 3. Introduction to Solar PV Participation in Frequency
Regulation 16
point (at VMPP + V and P1). In this figure, the power reduction was
obtained through a voltage increase V. The available reserve power
will thus correspond to the subtraction of the new operating point
power P1 from the maximum extractable power PMPP.
When using the reserve capacity of the PVs for frequency control
combined with fast reaction of PV plants, the conventional
generators will only be used for further frequency regulation once
the PVs capacity is fully used. This prioritization of the PV
generation is designed to reduce the conventional generators’
ramping and emissions. The authors in [26] propose a control
strategy to de-load the PV considering the amount of reserve
capacity of each PV plant, so that frequency control is performed
proportionally to the available power in each PV plant.
Figure 3.3: PV operation outside maximum power point. Source:
[26]1.
Secondary Frequency Control in an Unreliable Communication Network
Scenario
The most common secondary frequency control relies on a centralized
controller to send the frequency correcting signals to each of the
generating units that participate in secondary frequency regulation
and thus requires a reliable communication network. A supervisory
control and data acquisition (SCADA) system can be used in a
centralized strategy and connect the on-site equipment to the
off-site regulators such as utilities and grid operators.
In remote islands, typically no communication system exists for the
secondary control dispatch, due to high costs. In such cases the
dispatch is performed manually. A less reliable channel could be
used, for instance the internet, however the reliability of such
communication networks may be compromised. Therefore, alternative
methods have been proposed for a decentralized secondary frequency
control, in which each participating generating unit has a local
secondary frequency controller, reducing the communication
requirement for the secondary control layer and thus increasing the
system’s reliability. Nevertheless, even in secondary controls
methods without a communication layer such as the one proposed in
[20], communication is still required for black start coordination
and real-time monitoring. In Figure 3.4, the centralized and
decentralized topologies for the secondary frequency regulation can
be seen [30].
1Reprinted from International Journal of Electrical Power Energy
Systems, 60, P.P. Zarina,S. Mishra,P.C. Sekhar, Exploring frequency
control capability of a PV system in a hybrid PV-rotating
machine-without storage system, pp. 258-267, Copyright (2014), with
permission from Elsevier.
Chapter 3. Introduction to Solar PV Participation in Frequency
Regulation 17
(a) (b)
Figure 3.4: PV control topologies for secondary frequency control,
(a) centralized and (b) decentral- ized. Source: [30].
The main challenge of a decentralized secondary frequency control
is to adjust multiple controllers to restore the nominal frequency
in an efficient and orderly manner, without causing further system
instability. Decentralized control methods based on a distributed-
averaging proportional-integral (DAPI) controller and a consensus
technique are proposed in the literature. In the former, the
generating units will estimate their frequency, send it to some, or
all, of the other generating units and calculate the average of the
received information in order to determine the set-point for its
secondary controller. In this method, although a central controller
is not required, there is still a large communication exchange in
the network. The "consensus" or continuous-time distributed
averaging equation introduced in [31] can be used to calculate a
diffusive averaging term. This term can be included in the
secondary control, so that the inverters shift the droop
characteristic by the same amount and obtain accurate active power
sharing. In the consensus technique, communications are required
only with neighbouring generating units and are based on multiagent
systems theory [32].
In [33], a decentralized secondary control scheme is proposed for a
microgrid, composed of a grid-forming controlled inverter and
grid-following (PQ controlled) inverters. The former restores the
frequency by modifying their frequency reference and the latter by
modifying the active power reference in proportion to the frequency
error. The inverters are configured so that an active power sharing
ratio is maintained between the inverters and no unit is
overloaded. A grid-forming inverter is necessary when there is no
grid connection or other element in the system that regulates and
controls the frequency.
In [20] a control strategy for grid-forming inverters is proposed
with two configurations in order to achieve a fast transient
response as well as an accurate steady-state frequency restoration,
based on a time-dependent protocol. In droop-based primary control,
the angular frequency, ω, is reduced by increasing the active power
supplied, P , as per Equation 3.4 in which ω0 is the reference
angular frequency and m is the droop coefficient.
ω = ω0 −mP (3.4)
The instantaneous active and reactive powers are filtered using a
low-pass filter in order to decouple the droop control from the
voltage and current control systems [30]. The
Chapter 3. Introduction to Solar PV Participation in Frequency
Regulation 18
filtered active power, P , can be thus expressed as per Equation
3.5 [20]:
P = HLPF p = ωc
s+ ωc p, (3.5)
where ωc is the cut-off frequency, p is instant active power and
HLPF is the filter’s transfer function. Because the frequency is a
common variable shared by all generating units in the system, local
measurements are sufficient to ensure accurate active power sharing
in steady-state. This is not the case for reactive power sharing
due to different voltage amplitude throughout the grid. Therefore
uniform reactive power sharing may lead to undesired reactive power
flows in the grid [20].
A secondary control term, δ, is added to Equation 3.4 in order to
correct the steady- state error introduced by the primary control,
yielding Equation 3.6 and the control block diagram represented in
Figure 3.5 [20]. As can be seen in Equation 3.6, the secondary
control contribution is in parallel with the primary control
contribution.
ω = ω0 −mP + δ (3.6)
c© 2017, IEEE
Figure 3.5: Primary and secondary control block diagram. Source:
[20].
In primary frequency control, when using the same droop control
slope in different PVs, equal active power sharing will be obtained
in all PV units. The droop coefficients must be selected
proportionally to ensure active power sharing, as per Equations 3.7
and 3.8. This indicates that although the operation of the
frequency control is decentralized, the droop coefficients must be
selected having a global knowledge of the system [32]. Due to the
proportionality represented in Equation 3.7, a smaller droop
coefficient implies a steeper droop curve and thus yields a higher
active power reduction. The droop coefficient is thus a measure of
each generator’s participation in frequency regulation.
Pimi = Pjmj ,∀i, j ∈ νI (3.7)
Pi
Pimax
= Pj
Pjmax
,∀i, j ∈ νI (3.8)
Chapter 3. Introduction to Solar PV Participation in Frequency
Regulation 19
where νI is the set of inverter nodes, mi and mj are the droop
constants of inverters i and j, Pimax and Pjmax are the ratings of
inverter i and j and Pi and Pj are the I and j inverters’ nominal
active power injection.
The transfer function for δ was initially proposed as a P
controller, based on a low pass filter with an additional pole for
high frequency attenuation and control gains k0 and k1, given by
Equation 3.9 [20]. A low-pass filter is used in order to decouple
the primary and secondary control loops [30].
δ = k1
s+ k0k1 (ω0 − ω) (3.9)
With this control model, the frequency error in steady-state is
given by Equation 3.10 [20].
e0 = ω0 − ω = k0mP
1 + k0 (3.10)
A modified time-dependent control scheme has been proposed in [20],
in order to overcome the trade-off observed in Equations 3.9 and
3.10, for which a faster frequency response yields a higher error.
The controller, represented in Equation 3.11, with gains k1 and
k(t), will switch between a filtered proportional controller and an
integral controller.
δ(t) = k1
The sign function, sgn, is defined in Equation 3.12 [20].
sgn =
0, k (t) = 0. (3.12)
Equation 3.11 can be represented in the Laplace domain as shown in
Equation 3.13. For positive values of the cut-off frequency of the
low-pass filter, given by k(t), the control is performed by a
proportional controller. The low steady-state error is also
achieved for values of k(t) close to zero, for which the controller
will behave like an integral controller. If the switching
characteristic of the proposed control given by sgn was not
present, the integral control might lead to an unstable response
due to small frequency errors leading to an cumulative value of δ.
For values of k(t) equal to zero, δ will assume a constant value C,
which corresponds to the last value calculated [20].
δ =
{ k1
C, k (t) = 0. (3.13)
The time-dependent value k(t) will determine the cut-off frequency
of the low-pass filter and thus the steady-state frequency error.
Higher values of k(t) leads to faster dynamics but higher frequency
errors. With the proposed switch control in [20], higher values of
k(t) are used immediately after frequency deviation event
detection, in order to obtain a fast transient response, and
decrease linearly to zero after a total time of ct+ramp, to reduce
the steady-state error to negligible values, as shown in Figure
3.6. The constant section of the gain curve allows time for fast
active power sharing, whereas the ramp section allows for a
smoother transaction between primary and secondary frequency
control actions. The
Chapter 3. Introduction to Solar PV Participation in Frequency
Regulation 20
c© 2017, IEEE
Figure 3.6: Time-dependent controller gain for frequency control in
(top) single event and (bottom) multievent detection scenario.
Source: [20].
event is detected when the frequency or the active power signal
deviates by more than a predetermined limit, as suggested by [20].
Four parameters must be chosen: k1, kmax ct
and ramp.
4 | Research Methodology
4.1 Research Hypothesis The research hypothesis is that a small
island hybrid system can be optimized with a high share of PV
generation in order to meet a renewable target, and that the
frequency stability of such a system can be improved by including
PV participation in primary and secondary frequency regulation,
without communication requirements between the generator units in
the system.
The economic optimization of the installed capacity of generators
in the hybrid system, as well as the parameters of the PV frequency
control are selected for the case study, using real economic and
technical data from the existing system. Different operation
scenarios are used in order to verify the system stability and test
the research hypothesis.
4.2 Methodology Overview The case study consists of an Indonesian
island’s electrical system. The research was conducted in the two
stages described below.
In the initial stage, data from the island adopted as a case study
was collected and analyzed, including the costs specific to the
island as well as technical specifications of the existing
electrical system. This data was used as input to the tool HOMER,
which was used in order to optimize the system’s future installed
capacity of generators aiming to meet the 23% renewable target by
2025.
The second stage of the research corresponded to a frequency
stability analysis of the optimal scenario resulting from the first
stage. The island’s 2025 electrical system was designed in
PowerFactory using data from the current system as well as the
installed capacity optimized in the first stage. Frequency control
for the PVs was designed in PowerFactory and added to the PVs in
the island system. The system frequency stability was analyzed in
order to validate the designed expansion scenario or to demand a
new scenario selection from the first stage. The flowchart of the
optimal scenario selection methodology is shown in Figure 4.1. It
depicts a combined analysis using the tools HOMER and
PowerFactory.
In this chapter, the case study characteristics will be introduced,
and each of the research steps will be described in details.
21
Collect economic and tech- nical data from the island
Optimize the 2025 generation to meet renewable target,
using HOMER
Model 2025 island’s grid in PowerFactory, using optimized
installed generation capacity
Perform frequency stability analysis of the island grid, using
PowerFactory
Is the scenario feasible?
Optimal generation expansion scenario found
Design PV control strategy, using PowerFactory
Add controller to the PV model on the 2025 island system
no
yes
Chapter 4. Research Methodology 23
4.3 Introduction to Case Study Indonesia comprises over 17 000
islands, out of which approximately half are inhabited, and in
which half of the population lives in rural areas [34]. The
percentage of households connected to the grid (electrification
ratio) has improved significantly in the past few years, rising
from 73,7% (2011) to 92.8% (mid 2017) [35]. The remaining
percentage represents the greatest challenge, as it corresponds to
households in remote locations, thus representing higher investment
costs. The majority of rural areas are supplied by local diesel
generators, as the low demand in these areas does not justify the
investment cost of an infrastructure for an electricity grid.
Within the renewable energy technologies available, solar
photovoltaic is still a small percentage of the generation
installed capacity in Indonesia, representing 0,63% of the
renewable generation capacity installed in 2017. Nevertheless, the
installed capacity of this technology has increased in the past 10
years, rising from 5,7 MW in 2007 to 58,06 MW in 2017. In 2017, 71%
of the installed capacity of solar photovoltaic technology in
Indonesia was located in off-grid systems, mainly PV-battery
systems powering small villages [25].
The Indonesian government has set targets for the renewable energy
generation to achieve 23% of renewable energy sources by 2025 and
31% by 2050. Renewable energy sources include geothermal resources,
hydropower, bioenergy, solar, wind and ocean energy. Within the 23%
target of renewable energy utilization, the Ministry of Energy and
Mineral Resources (MoEMR) has set subtargets of 10% bioenergy, 7%
geothermal, 3% hydropower, and 3% others [36]. In case the
renewable target is not met, the remaining renewable generation
target will be fulfilled by gas. In order to meet the targets, the
“35 GW Programme” was created at the end of 2014 and aims to
complete 35 GW of power generation projects within 5 years.
An Indonesian island was selected for a large-scale renewable
integration scenario analysis, in which the expansion of the
generation will be optimized in order to meet the 2025 national
renewable target. The results from the technical-economic analysis
performed can be used as lessons learned in the generation
expansion planning of similar isolated islands. Although the
island’s identity must remain undisclosed in this report, this must
not impact the reader’s understanding of it. The selected island
will be referred to as "the island" throughout this report. The
characteristics of the island’s system are described in Table
4.1.
Table 4.1: Characteristics of the pilot island.
Parameter Value
Population 13 000 (with 4000 Households) Load 0,8 MW peak
3 GWh (2017) Supplied 12 hours/day
Classification Very small grid Power plants 1,312 MW (100% Diesel)
Fuel consumption and cost 94 500 liters/month average
0,589 EUR/Liter Generation cost 0,21 EUR/kWh (2017)
Chapter 4. Research Methodology 24
Today, the electricity supply of the island is limited to 12 hours
per day due to its high cost, with a 13,5 h supply on Sundays. Two
new Cummins generators (model C900D5, engine QSK23G3, 656kW/820kVA
Prime Rating) have been installed recently, which replaced the
previous less efficient and aged generators. A sample power curve
of a Sunday during the partial supply regime in 2017 can be seen in
Figure 4.2.
0
100
200
300
400
500
600
700
800
900
7 8 9 10 11 12 13 14 15 16:30 18:00 19:30 21:45 23:45 04:30
06:00
D e
m an
d [
kW ]
Hour
Demand in the partial supply regime (Sunday 13,5h supply in
2017)
NO SUPPLY
Figure 4.2: Island’s demand measured sample during a partial supply
regime.
During the commissioning of the new generators, trial periods were
run with a 24 h supply. A load data sample during the 24 h regime
can be seen in Figure 4.3. This load sample will be used as base
data to project the load increase for the year of 2025, in which a
24 h supply will be in place.
0
100
200
300
400
500
600
700
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
D e
m an
Demand in the 24h regime (January 2018)
Figure 4.3: Island’s demand measured sample during a 24 h supply
regime.
Renewable Generation Potential
The overall renewable energy potential in the island region can be
analyzed using data available in [37]. The capacity factor of a
power plant represents the ratio of the expected production over a
period of time to the maximum production if operating at
nameplate
Chapter 4. Research Methodology 25
capacity. The daily mean capacity factor for the solar energy in
the case study island area is depicted in Figure 4.4, with a total
mean capacity factor of 16,5%1.
Corresponding data can be found on Figure 4.5 for the wind
potential, and illustrates a very low total mean capacity factor of
2,90%2. The low capacity factor observed for wind energy in this
region makes the installation of wind turbines in the island
economically unfeasible.
PV System Daily Mean Capacity Factor
Figure 4.4: Daily mean capacity factor of a sample solar PV system
in case study location. Annual average of 16,5%. Source:
[37]3.
Wind Turbine Daily Mean Capacity Factor
Figure 4.5: Daily mean capacity factor of a sample wind turbine in
case study location. Annual average of 2,90%. Source: [37]3.
Solar irradiance measurements from an Indonesian location with
similar conditions to those of the case study, shown in Appendix
A1, indicated variations of up to 810 W/m2
within one minute, with an average daily irradiance of 420 W/m2.
These will be used as reference for the rate of irradiance change
per minute in the simulations.
1No tracking, zero tilt and 10% system losses were considered in
order to estimate the output. 2The small wind turbine Enercon E40
600 kW, with maximum hub height of 65 meters [38] was used to
estimate the results. 3License to reproduce image granted under the
public license: https://creativecommons.org/licenses/by-
nc/4.0/legalcode
Chapter 4. Research Methodology 26
4.4 Optimization of Generation Expansion
4.4.1 Structure of the Optimization Problem As shown in the
flowchart of Figure 4.1, in order to obtain an optimal scenario for
the generation installed capacity, an optimization problem must be
structured using the island’s specific technical and economic
parameters. An optimization problem structure contains parameters,
variables, constraints, limits and an optimization function. The
tool selected to model and solve the optimization problem in the
case study was the Hybrid Optimization for Multiple Energy
Resources (HOMER) software, originally developed by the NREL.
The software HOMER is an optimization tool to select the optimum
generation mix based on selected grid components and input
parameters such as discount rate, generation costs, fuel costs,
capital costs, load characteristics (annual average [kWh/day],
daily profile characteristics), energy storage characteristics,
among others. The tool searches for the minimum value of the
optimization variable, in this project defined as the net present
cost of all the costs in the system.
The optimization problem will minimize the Levelized Cost of Energy
(LCOE) in the system by choosing the generation sources and
corresponding dispatch that will meet the load demand with the
lowest cost, whilst meeting the constraints defined by the user.
The tool allows setting constraints for the operating reserve as a
percentage of load or renewable generation, emission limits as well
as for the minimum renewable energy contribution to the total
yearly energy production. The structure of the optimization problem
in HOMER can be found in Table 4.2.
The values selected for the parameters and constraints of the
optimization problem were defined based on the Indonesian national
targets, as well as on data collected from the island. These values
are input to the tool HOMER and are summarized in the following
sections (4.4.2 and 4.4.3).
Chapter 4. Research Methodology 27
Table 4.2: Optimization problem structure.
Type Object
Parameters Equipment’s initial capital cost Equipment’s O&M
cost Equipment’s replacement cost (replacement at the end of its
lifetime) Equipment’s lifetime Generator’s minimum load ratio
Diesel fuel price Fuel curve (input as liters/hour, per output
power in kW) Equipment’s installed capacity (if already installed
in the system) 24 h load profile (kW) Day to day variability
(%)1
Nominal discount rate (%) Expected inflation rate (%) Project
lifetime 2
Constraints Minimum renewable energy fraction (%)3
Operating reserve (as a percentage of load and/or solar power
output) 4
Emissions
Variables Installed capacity of different system components (diesel
generators, PV panels, converter and ESS) LCOE, NPC, operating
cost, initial capital cost Total fuel consumption (liters/year)
Dispatch and operating hours per source Percentage of renewable
fraction achieved in the production Dispatch and operating hours
per source
4.4.2 Optimization Parameters
Renewable Resource Parameters
In HOMER, the user can insert data for the renewable resources
available or select the geographical location of the project. The
latter option was used, in which the tool collects the location’s
renewable resource data from the NASA Surface Meteorology and Solar
Energy Database. This data can also be accessed online at
[39].
Section 4.3 introduced the potential of the renewable resources
available in the island’s region, highlighting that the wind
potential on the island area is unfavorable to the installa- tion
of wind turbines. This was confirmed from initial simulations
results, after which wind
1Variability randomly added to the load to obtain unique daily load
profiles. The load value entered by the user is multiplied by a
random value from a normal distribution with mean 1 and standard
deviation equal to the day to day variability.
2Duration of the project during which costs occur. Salvage values
of components are accounted for at the end of the project’s
lifetime.
3Annual share of the generation supplied to the load that was
originated from renewable energy. 4Surplus of operating capacity
that can be used instantly in case of load or generation sudden
variation.
Chapter 4. Research Methodology 28
turbines were removed from the optimization problem, in order to
reduce the simulation time.
Load Parameters
The load was scaled to 2025 based on the growth forecast of the
island’s population, using the sampled data for the demand when the
supply is 24 h (shown in Figure 4.3). An additional load increase
was accounted for, because the loads and consumption profile are
expected to change when moving from a partial electricity supply to
a 24 h supply. For example, during the 24 h supply trial period
consumers already bought new appliances. A 20% load increase per
year was considered. Although this value might seem high it would
result in a consumption of only half the Indonesian average by
2025. The load forecast data can be found in Table 4.3. This load
considers a variability of 5%, in order to introduce a randomness
to the daily load.
Table 4.3: Load forecast scenario with 20% load increase per
year.
Year Consumption [GWh/year] Demand [MW]
2018 3,8 0,8 2019 4,6 1 2020 5,5 1,2 2021 6,6 1,4 2022 7,9 1,7 2023
9,5 2,1 2024 11,4 2,5 2025 13,7 3
ESS, PVs and Diesel Generator Parameters
The technical parameters of the system’s components are listed in
Table 4.4. The new generators are modelled with the same
characteristics of the existing generators.
Economic Parameters
In HOMER, the Levelized Cost Of Energy (LCOE) is calculated by
dividing the annualized cost of the electricity production in the
system by the total load served. The annualized cost is the annual
value of the net present cost, calculated by the product of the
total NPC and a the Capital Recovery Factor (CRF), the latter a
function of the nominal discount rate and the expected inflation
rate parameters [40], [41]. These parameters were selected as the
average of historical Indonesian values available at [42] and [43].
These values, the transportation cost considered for total
equipment costs and the conversion rate to Indonesian Rupiah (IDR)
used for quotations obtained in Euros are listed in Table
4.5.
Chapter 4. Research Methodology 29
Table 4.4: Component’s technical parameters.
Component Parameter Value
GENERATOR
Model Cummins C900-D5, engine QSK23G3 Minimum load ratio 10% Prime
rating 656 kW/ 820 kVA Standby rating 720 kW/ 900 kVA Lifetime 25
years
Fuel curve 1 Output [kW] Consumption [L/hour]
164 46 328 85 492 121 656 161
PVs
Lifetime 25 years Derating factor2 80% Ground reflectance3 20% No
tracking system Temperature effects on power3 -0,5 [%/C ] Nominal
operating cell temperature3 47 [C] Efficiency at STC3 13%
LI ION INVERTER 3
Inverter input Lifetime: 15 years Efficiency: 95%
Rectifier input Relative capacity: 100% Efficiency: 90%
LI ION 3 Lifetime 15 years Initial state of charge 100% Minimum
state of charge 20%
Table 4.5: Rates for the system’s cost parameters.
Cost parameter Value
Inflation rate [%] 4 Nominal discount rate [%] 8 Conversion rate
(on 14.03.18) [IDR/EUR] 16973 Transportation cost to the island4
[EUR/kg] 1,473
1Data from the manufacturer’s equipment datasheet (Cummins C900D5).
2The derating factor reduces the PV output based on different
aspects, including aging, inverter and trans-
former, shading, wiring, etc. 80% is used in the installation year
(approximated from the 0,77 suggested by NREL’s PVWatts [44]). An
additional 1% loss per year was considered for the PV panels, based
on aging.
3HOMER default parameters. 4The transportation cost of the existing
generators to the island was used as reference for future
equipment,
added to their capital cost.
Chapter 4. Research Methodology 30
Research was conducted on current and forecasted costs for PVs, Li
Ion battery systems and diesel generators. The data gathering
focused on current prices in Indonesia as well as on international
price forecasts. The conversion rate given in Table 4.5 was used
when necessary and the transportation cost shown was used in order
to obtain component costs specifically for the island. The economic
parameters obtained, and used as inputs to HOMER, are listed in
Tables 4.6 and 4.7. The capital cost forecast is based on both
technology and installation cost reductions.
Table 4.6: Capital cost forecast of the hybrid system
components.
Year PV 1 Battery Inverter4 Batteries (Li Ion) 1 Diesel Generator
5
CC2 CC+tr36 CC CC+tr6 CC CC+tr7 CC+tr 2018 810 990 389 429 272,84
283,15 243,24 2019 758 939 364 404 263,03 273,34 243,24 2020 710
891 341 381 253,57 263,88 243,24 2021 665 846 320 359 244,45 254,76
243,24 2022 623 803 299 339 235,66 245,97 243,24 2023 584 764 280
320 227,19 237,50 243,24 2024 547 727 263 302 219,01 229,32 243,24
2025 512 692 246 286 211,14 221,45 243,24
Table 4.7: Operational costs of the hybrid system components.
Year PV1 Battery Inverter Battery Li Ion1 Diesel Generator5
2018 - 2025 O&M8 O&M O&M Fuel (EUR/L) O&Mv9
O&Mf10
12,16 0 5,7 0,589 0,0097 15000
1Costs calculated based on data from [45]. 2CC: Capital Cost in
[EUR/kW] for PV, inverter and diesel generator and in [EUR/kWh] for
the batteries. 3CC+tr: Capital Cost including transportation cost
to the island, same unit as component’s CC. 4Costs from the
quotation of an existing PV plant in the vicinity of the island.
5Costs obtained from the purchase and maintenance contracts of the
existing units on the island. 6The year reduction considered was
9%[46] on equipment cost (67% of total, excluding transportation
costs)
and -1% on the remaining percentage (installation costs)[45]. 7The
year reduction considered was 4,5%[47] on equipment cost (74% of
total, excluding transportation
costs) and -1% on installation costs[45]. 8O&M: Operation and
maintenance Cost in [EUR/kW,yr] except for Li Ion batteries, in
[EUR/kWh,yr]. 9O&Mv: Operation and maintenance variable cost
[EUR/kW,hr].
10O&Mf: Operation and maintenance fixed cost [EUR/yr],
corresponding to staff costs.
Chapter 4. Research Methodology 31
4.4.3 Optimization Constraints The constraints selected for the
optimization problem are summarized in Table 4.8. The first
constraint in the system is that no capacity shortage is allowed,
in order to guarantee that every peak load can be met by the
generation installed capacity.
The minimum renewable fraction desired to be achieved is the second
constraint defined by the user. This corresponds to the fraction of
the energy delivered to the load that is produced from renewable
sources. For the year 2025, this value corresponds to the
Indonesian target.
The third constraint corresponds to the operating reserve. In a
system with a high share of renewable production it is important to
have a higher operating reserve in order to cover sudden variations
in the renewable production in addition to the load variation,
increasing the reliability of the system. This operating reserve
can be provided by energy storage systems, rotating machines, the
grid, among others. In HOMER simulations, the tool attempts to
respect the constraint set for the reserve requirements at each
time step, resulting in the operation of an installed capacity
larger than would be required to meet the load [48].
The operating reserve as a percentage of the solar power, was
defined based on the irradiance variations of up to 80% within a
minute, obtained from the data depicted in Figure 6.1, Appendix A1.
Higher values for the operating reserve will result in higher
costs. However, if the PVs are geographically distributed along the
island it is less likely that a cloud movement will affect all
units simultaneously with this irradiance variation, thus the
operating reserve constraint could be reduced. In the simulations,
a conservative approach was used by considering a value of 80%. A
constraint that at least one of the diesel generation must be
running at all times was also included.
Table 4.8: Optimization constraints.
Optimization Constraints
Maximum annual capacity shortage [%] 0 Minimum renewable fraction
[%] 23 Operating reserve as a percentage of load [%] 10 Operating
reserve as a percentage of solar power output [%] 80 Diesel-off
operation Not allowed
4.4.4 Simulation Structure in HOMER The optimization problem was
solved for a system with renewable energy as well as for a system
with diesel generators only, for comparison. In order to account
for decreasing prices in PV, ESS and converter technologies until
2025 (forecasted as per Table 4.6), a step-wise simulation
structure was developed in HOMER, allowing for the optimization of
a gradual installation of the renewable energy in the system until
2025. Two year steps were considered for each installation period,
resulting in 4 simulations per load scenario with renewable
penetration, as well as one additional simulation for the scenario
comprising diesel generators only.
Chapter 4. Research Methodology 32
The step-wise simulations were structured in a cascade, with the
optimal output of each simulation representing inputs to the next
simulation as shown in Figure 4.6. The optimized additional
installed capacities of the generation at the end of each 2 year
period becomes a fixed capacity in the following simulation, with
the new load increase to be covered by new installed generators or
by a capacity factor increase of the already installed
generation.
Figure 4.6: Step-wise simulation inputs and outputs in HOMER.
The cost input parameters for each 2-year step-wise simulation will
be the cost of the component in the first year of each simulation
period (installation year of that portion of renewable energy). The
step-wise simulations are optimized to supply the projected load of
the second year of each simulation period (maximum load of that
simulation period). The reference years for the cost and load
values used in each simulation can be seen in Figure 4.7. Intervals
of two years have been used in order to simplify the optimization
process. The consequences of these simplifications were higher
operation costs, due to the highest load (second year) being used
also for the first year of each simulation. The constraint of 23%
renewable generation was only included in the last simulation step
(year 2025) thus, in the remaining simulations, the installed
renewable generation was optimized based on costs only.
Simulation 1
1Includes installed capacity of all previous steps.
Chapter 4. Research Methodology 33
4.5 PV Frequency Control Strategy Whereas the tool HOMER can be
used to optimize the installed generation capacity for the island
grid accounting for economic parameters and system constraints, the
behaviour of the electrical system with the resulting installed
capacities must be further analyzed using a power system analysis
tool. In this section, the methodology utilized for the PV
secondary frequency control will be introduced, as well as the test
scenarios chosen for the frequency stability analysis of the island
system.
4.5.1 Proposed PV Model Features The designed PV control model is
based on the method proposed by [20], which was intro- duced in
Section 3.3, as well as in a previous model made by the company
Energynautics, which consists on an adaptation of the generic
three-phase PV model in Powerfactory. In Table 4.9, a comparison is
made between the main features of each model/method.
In the model previously adapted by Energynautics, the PVs were
designed to participate in LFSM-O, by curtailing their power output
in case of an over-frequency event that exceeded the frequency
dead-band. The PVs return to their maximum power point output once
the frequency decreases to a value within the hysteresis dead-band.
The power curtailment is performed based on a user pre-defined
droop curve and frequency dead-band setting. The PVs have no active
power reserve and thus operate by default on their MPP. This model
also includes reactive power control.
Compared to the previous Energynautics model and to the method
proposed by [20], the proposed model has the main advantages
of:
• Improvement of the system’s frequency response to under-frequency
events, by enabling the PV contribution to such events through the
allocation of a fraction of the PV’s installed capacity as a
reserve.
• Differentiation of the active power sharing between PVs and
diesel generators during the over and under-frequency regulation
process, through different over and under- frequency control
parameter settings of the PV units. During under-frequency events,
PVs should contribute more to the required generation increase, wh
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